Abstract

Purpose

To control for confounding bias from non-random treatment assignment in observational
data, both traditional multivariable models and more recently propensity score approaches
have been applied. Our aim was to compare a propensity score-stratified model with
a traditional multivariable-adjusted model, specifically in estimating survival of
hemodialysis (HD) versus peritoneal dialysis (PD) patients.

Methods

Using the Dutch End-Stage Renal Disease Registry, we constructed a propensity score,
predicting PD assignment from age, gender, primary renal disease, center of dialysis,
and year of first renal replacement therapy. We developed two Cox proportional hazards
regression models to estimate survival on PD relative to HD, a propensity score-stratified
model stratifying on the propensity score and a multivariable-adjusted model, and
tested several interaction terms in both models.

Results

The propensity score performed well: it showed a reasonable fit, had a good c-statistic,
calibrated well and balanced the covariates. The main-effects multivariable-adjusted
model and the propensity score-stratified univariable Cox model resulted in similar
relative mortality risk estimates of PD compared with HD (0.99 and 0.97, respectively)
with fewer significant covariates in the propensity model. After introducing the missing
interaction variables for effect modification in both models, the mortality risk estimates
for both main effects and interactions remained comparable, but the propensity score
model had nearly as many covariates because of the additional interaction variables.

Conclusion

Although the propensity score performed well, it did not alter the treatment effect
in the outcome model and lost its advantage of parsimony in the presence of effect
modification.